Data Scientist & ML Engineer |Developing TrustLens
"In God we trust, all others must bring data." – W. Edwards Deming
Building TrustLens
Because model accuracy alone is not enough.
TrustLens is an open-source framework for evaluating: reliability, calibration, fairness, failures, and explainability — in a single workflow.
from trustlens import analyze
report = analyze(model, X_val, y_val, y_prob=proba)Deep dives into the mathematical foundations of Machine Learning, Optimization, and AI — focused on intuition, derivations, and rigorous theory behind modern ML algorithms.



